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An EEG majority vote based BCI classification system for discrimination of hand motor attempts in stroke patients


Gu, X and Cao, Z, An EEG majority vote based BCI classification system for discrimination of hand motor attempts in stroke patients, 27th International Conference on Neural Information Processing, 18-22 November 2020, Virtual Conference, Online, pp. 1-8. (In Press) [Refereed Conference Paper]

Available from 18 November 2021

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Stroke patients have symptoms of cerebral functional disturbance that could aggressively impair patient's physical mobility, such as hand impairments. Although rehabilitation training from external devices is beneficial for hand movement recovery, for initiating motor function restoration purposes, there are still valuable research merits for identifying the side of hands in motion. In this preliminary study, we used an electroencephalogram (EEG) dataset from 8 stroke patients, with each subject conducting 40 EEG trials of left motor attempts and 40 EEG trials of right motor attempts. Then, we proposed a majority vote based EEG classification system for identifying the side in motion. In specific, we extracted 1-50 Hz power spectral features as input for a series of well-known classification models. The predicted labels from these classification models were compared and a majority vote based method was applied, which determined the finalised predicted label. Our experiment results showed that our proposed EEG classification system achieved 99:83 0:42% accuracy, 99:98 0:13% precision, 99:66 0:84% recall, and 99:83 0:43% f-score, which outperformed the performance of single well-known classification models. Our findings suggest that the superior performance of our proposed majority vote based EEG classification system has the potential for stroke patients' hand rehabilitation.

Item Details

Item Type:Refereed Conference Paper
Keywords:stroke rehabilitation, hand motor attemptsc EEG, classification, BCI, hand movement
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Health
Objective Group:Clinical health
Objective Field:Clinical health not elsewhere classified
UTAS Author:Gu, X ( Xiaotong Gu)
UTAS Author:Cao, Z (Dr Zehong Cao)
ID Code:141227
Year Published:In Press
Deposited By:Information and Communication Technology
Deposited On:2020-10-06
Last Modified:2020-11-12

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